Web mapping and web apps for soil data

library(sf)

## Linking to GEOS 3.5.1, GDAL 2.1.3, proj.4 4.9.2, lwgeom 2.3.2 r15302

library(raster)

## Loading required package: sp

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:raster':
## 
##     intersect, select, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(mapview)

## Loading required package: leaflet

library(RColorBrewer)

data('cookfarm', package = "GSIF")

names(cookfarm)

## [1] "readings"    "profiles"    "bdensity"    "grids"       "weather"    
## [6] "proj4string"

grids <- cookfarm$grids %>% 
  dplyr::select(x, y, DEM, TWI, Cook_fall_ECa, Cook_spr_ECa) %>% 
  rasterFromXYZ(crs = cookfarm$proj4string)

plot(grids)

profiles <- cookfarm$profiles %>% 
  st_as_sf(coords = c('Easting', 'Northing'), crs = cookfarm$proj4string)

plot(profiles)

# Plot points
mapview(profiles)

mapview(profiles, zcol = "BLD")

mapview(profiles, zcol = "TAXSUSDA")

pal_continuous <- colorRampPalette(brewer.pal(7, "BrBG"))
pal_categorical <- colorRampPalette(brewer.pal(9, "Set1"))

mapview(profiles, zcol = "BLD", col.regions = pal_continuous, legend = TRUE)

mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical, legend = TRUE)

# Plot grids
mapview(grids)

mapview(grids, col.regions = pal_continuous, legend = TRUE)

mapview(grids$TWI, col.regions = pal_continuous, legend = TRUE)

# Plot both together
mapview(grids$DEM, col.regions = pal_continuous, legend = TRUE) + mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical)

# Burst to separate soil classes
mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical, legend = TRUE, burst = TRUE)

# Syncing several maps
m1 <- mapview(grids$DEM)
m2 <- mapview(grids$TWI)
m3 <- mapview(profiles)

sync(m1, m2, m3)

# Raster specific functions
viewRGB(poppendorf)

viewRGB(poppendorf, 4, 3, 2)

# plainview
plainview(poppendorf[[1]])

plainview(poppendorf[[1]], at = seq(8000, 11000, 50))

plainview(poppendorf, 4, 3, 2)

plainview(poppendorf, 5, 4, 3, quantiles = c(0.5, 1))

# slideview
slideview(poppendorf[[1]], poppendorf[[5]])

slideview(poppendorf[[1]], poppendorf[[5]], legend = FALSE)

img1 <- poppendorf[[1]]
img2 <- poppendorf[[5]]

slideview(img1, img2,
          label1 = "Poppendorf-Layer-1",
          label2 = "Poppendorf-Layer-2")

# cubeview
# stck <- raster::stack("tappelhans/uni/talks/jena_201703/data/barazar.tif")
# raster::nlayers(stck) * raster::ncell(stck) # takes a while!!
# cubeview(stck)

mapview(
  profiles, 
  popup = popupImage('https://www.vcard.wur.nl/WebServices/GetMedia.ashx?id=37263')
)

Advanced mapview

library(xts)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

library(dygraphs)

profiles$SOURCEID <- as.character(profiles$SOURCEID)

records <- cookfarm$readings
records$SOURCEID <- as.character(records$SOURCEID)

ids <- unique(records$SOURCEID)

# Subset sensors
ids <- sample(ids, size = 5) 
  
idx_sensors <- which(profiles$SOURCEID %in% ids)
sensors <- profiles[idx_sensors,]

make_ts <- function(id) {
  
  records %>% 
    filter(SOURCEID == id) %>% 
    dplyr::select(-SOURCEID) %>%
    dplyr::select(Date, ends_with('VW')) %>% 
    xts(.$Date)
}

make_dygraph <- function(id){
  ts <- make_ts(id)
  dygraph(ts)
} 

l_graphs <- lapply(
  ids,
  make_dygraph
)

make_dygraph(ids[1])

mapview(sensors, popup = popupGraph(graphs = l_graphs, width = 300, height = 300))